Information overload is no longer a theoretical concept, but a real impediment to education, research and patient care. Our long-term goal is to improve patient care by providing better information retrieval tools to students, researchers and clinicians. Our unifying hypothesis is that techniques pioneered on the World Wide Web (WWW) can be successfully adapted to the combination of MEDLINE and the Science Citation Index (SCI) to improve information retrieval. The combination of MEDLINE and SCI is a hyper linked environment similar to the WWW. Therefore, successful WWW algorithms can be applied to identify the most important and relevant articles to fulfill users' information needs. The specific aims of this proposal are: 1) To evaluate the benefit of citation analysis for ranking MEDLINE search results and 2) To evaluate the benefit of citation analysis for determining article similarity in MEDLINE. This research is a continuation of work performed during NLM fellowship training at Stanford where we designed and implemented the Medline Query-by-Example (MQBE) computational framework. MQBE framework will be used to minimize the human effort required to implement and evaluate information retrieval strategies. The computational framework will be enhanced to store statements of information need, queries and relevance judgments of real users who are using the system to fulfill real information needs. Result ranking algorithms will be evaluated with respect to their ability to preferentially return "key articles" selected by panels of experts. In addition, a new evaluation methodology developed by Joachims that uses click throughs to compare alternative search strategies will be employed to compare algorithms to each other. The data collected in this proposal will serve as the foundation for a sustainable general research effort focusing on information retrieval.